Increasing advances in computer technology (e.g., microprocessor speed, memory capacity, data transfer bandwidth, software functionality, and the like) have generally contributed to increased computer application in various industries. Ever more powerful server systems, which are typically configured as an array of servers, are commonly provided to service requests originating from external sources such as the World Wide Web, for example. As local Intranet systems have become more sophisticated, thereby requiring servicing of larger network loads and related applications, internal system demands have grown accordingly as well. As such, much business data is stored in data stores, under a management system.
The amount of available electronic data is continuously growing, and it has become ever increasingly important to store such data in data stores in a manageable manner, which facilitates user friendly, and quick data searches and retrieval. In general, a data store can be referred to as an organized collection of information with data structured such that a computer program, for example, can quickly search and select desired pieces of data. Data within a data store can be organized via one or more tables, wherein respective tables comprise a set of records, and a record can comprise a set of fields. Records are commonly indexed as rows within a table and the record fields are commonly indexed as columns, so that a row/column pair of indices can reference a particular datum within a table. Typically, such data stores can be viewed as organized collection of related information stored as “records” having “fields” of information therein.
At the same time, conventional data stores and operating systems have typically relied on multiple incompatible storage for data, including; the registry, event log messages, contact information, and e-mail, or simply have used multiple flat files for data such as images and audio. For example, in conventional data stores, stored contents are in general treated as separate entities, even though they are interrelated at some level. Accordingly, when a large number of items exist, it can become important to have a flexible and efficient mechanism to search for particular items based on their properties and content. For example, it can be desirable for knowledge workers to be able to search for contents, independent of format and regardless of what type of a file a particular content is, and what application created that.
Given a new file system that operates based on relational objects with an extensible data type, new challenges can arise. For example, in such environments, a data model can play an important role in the way that participants interact with the database. Moreover, a manner for which an application stores and retrieves data can be governed by the data model.
Interactions involved with data handling of such a data store can involve various parties and different data models. Accordingly, existence of different models can hinder proper interaction between the participants, as data may not be properly representable to all parties involved. Moreover, converting from one data representation to another can typically become time consuming and resource intensive, while at the same time be fraught with conversion problems, and in some cases, totally impracticable due to the complexity.
Therefore, there is a need to overcome the aforementioned exemplary deficiencies associated with conventional systems and devices.